American Geriatrics Society Abstracted Clinical Practice Guideline for Postoperative Delirium in Older Adults
Bibliographic record
Abstract
The abstracted set of recommendations presented here provides essential guidance both on the prevention of postoperative delirium in older patients at risk of delirium and on the treatment of older surgical patients with delirium, and is based on the 2014 American Geriatrics Society (AGS) Guideline. The full version of the guideline, American Geriatrics Society Clinical Practice Guideline for Postoperative Delirium in Older Adults is available at the website of the AGS. The overall aims of the study were twofold: first, to present nonpharmacologic and pharmacologic interventions that should be implemented perioperatively for the prevention of postoperative delirium in older adults; and second, to present nonpharmacologic and pharmacologic interventions that should be implemented perioperatively for the treatment of postoperative delirium in older adults. Prevention recommendations focused on primary prevention (i.e., preventing delirium before it occurs) in patients who are at risk for postoperative delirium (e.g., those identified as moderate-to-high risk based on previous risk stratification models such as the National Institute for Health and Care Excellence (NICE) guidelines, Delirium: Diagnosis, Prevention and Management. Clinical Guideline 103; London (UK): 2010 July 29). For management of delirium, the goals of this guideline are to decrease delirium severity and duration, ensure patient safety and improve outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.014 | 0.014 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".